Analyze DORA metrics

Navigate and analyze your organization’s DORA metrics to assess deployment performance, compare against industry benchmarks, and identify specific areas for delivery improvement. This guide covers dashboard access, filtering, metric interpretation, and performance analysis workflows.

Before you begin, ensure you have completed analytics dashboard setup and have active deployment data from workflows with kind: deploy annotations.

Access and configure DORA metrics view

DORA metrics analysis requires proper component and environment scoping to provide meaningful performance insights. To access and configure your DORA metrics view:

Navigate to Analytics DORA metrics.
Select FILTER to configure your analysis scope.
Configure component selection:
1. Select one or more Components from the available options
2. Choose specific components for focused analysis or multiple components for comparative assessment
Select target Environment from your configured environments.

Set analysis duration from the following options:

Table 1. **Duration** filter definitions
Duration	Definition
Current week	Current week in the month, Monday to Sunday schedule. For example, if current day is Tuesday, only data from Monday and Tuesday are displayed.
Previous week	Previous week in the month, Monday to Sunday schedule.
Two weeks back	Two weeks prior in the month, Monday to Sunday schedule.
Current month	First day of current month up to current day.
Previous month	First day to last day of previous month.
Two months back	First day to last day of two months prior.
Last 7 days	The past seven days.
Last 30 days	The past 30 days.
Last 90 days	The past 90 days.
Custom range	Select a custom range

Select APPLY to update the dashboard with your filtered data.

The dashboard displays four key metrics with performance ratings and percentage changes from the previous timeframe.

Set a custom date range

To set a custom date range:

Select FILTER.
Select Custom range.
Select dates for the time frame start and end.

The custom date range is set accordingly and displayed in blue on the date picker. You can view the analytics data for any desired time frame.

DORA metrics calculations depend on workflow runs with kind: deploy annotations and proper environment associations configured during setup.

Interpret deployment frequency metrics

Deployment frequency measures your organization’s capability to deliver changes to production and indicates development velocity. To analyze deployment frequency performance:

Review the deployment frequency calculation displayed as average deployments per day.
Compare your frequency against DORA performance benchmarks:
1. Elite performers: Multiple deployments per day
2. High performers: Daily to weekly deployments
3. Medium performers: Weekly to monthly deployments
4. Low performers: Monthly to every six months
Select the Average per day number link to drill into successful deployments details.
Review the deployments list containing:
1. Run ID and component information
2. Run start time and deployment completion time (UTC)
3. Lead time calculation for each deployment
4. Associated workflow information
Identify deployment patterns such as:
1. Consistent daily deployment cadence vs. sporadic releases
2. Business hours deployment restrictions vs. continuous deployment capability
3. Component-specific deployment frequency variations

Higher deployment frequency typically indicates mature development practices including automated testing, deployment automation, and effective change management processes.

Analyze deployment lead time

Deployment lead time measures how quickly changes move from commit to production deployment, indicating process efficiency. To assess lead time performance:

Review current lead time measurement showing average time from code change to deployment.
Select the Average duration link to display a list of successful deployments for the selected component and environment.
Compare against DORA lead time benchmarks:
1. Elite performers: Less than one hour to one day
2. High performers: One day to one week
3. Medium performers: One week to one month
4. Low performers: One month to six months
Examine the deployment frequency and lead time trend chart to identify:
1. Lead time consistency over the selected time period
2. Correlation between deployment frequency and lead time variations
3. Seasonal or cyclical patterns in development delivery
Analyze lead time spikes by correlating with:
1. Deployment complexity changes
2. Process modifications or tool updates
3. Team capacity variations or organizational changes

Shorter lead times enable faster feedback loops and reduce the risk associated with individual changes by making deployments smaller and more frequent.

Monitor change failure rate and recovery

Change failure rate and mean time to recovery (MTTR) balance deployment speed metrics with quality and organizational resilience. To analyze quality and recovery metrics:

Review change failure rate percentage showing failed deployments relative to total deployments.
Compare failure rate against benchmarks:
1. Elite performers: 0-15% failure rate
2. High performers: 16-20% failure rate
3. Medium performers: 21-45% failure rate
4. Low performers: 46-60% failure rate
Examine mean time to recovery (MTTR) measurements.
Compare MTTR against performance benchmarks:
1. Elite performers: Less than one hour recovery time
2. High performers: Less than one day recovery time
3. Medium performers: Less than one week recovery time
4. Low performers: One week to one month recovery time
Analyze the failure rate and MTTR trend charts to identify:
1. Correlation between deployment frequency increases and failure rate changes
2. Recovery time improvements or degradations over time
3. Patterns indicating process maturity or technical debt accumulation

Effective organizations balance deployment speed with stability by maintaining low failure rates while achieving fast recovery when issues occur.

Compare performance across components and environments

Component and environment comparison reveals performance variations and identifies improvement opportunities across organizational boundaries. To conduct comparative performance analysis:

Configure multi-component comparison by selecting multiple components in the filter.
Analyze performance differences between:
1. Different development teams or applications
2. Various technology stacks or architectural patterns
3. Different organizational units or product lines
Compare environment-specific performance patterns:
1. Staging vs. production deployment frequency variations
2. Environment-specific failure rates indicating deployment process differences
3. Lead time variations between different deployment targets
Identify high-performing and underperforming areas by examining:
1. Components consistently achieving elite or high performance ratings
2. Components with performance gaps requiring attention
3. Environmental factors affecting deployment success rates
Export comparison data using dashboard export functionality for:
1. Executive reporting on organizational performance trends
2. Team-specific improvement planning and goal setting
3. Historical tracking of performance improvements across components

Performance variations often reveal opportunities for knowledge sharing, process standardization, or targeted capability investments.

Use DORA data for improvement planning

DORA metrics provide strategic guidance for development capability improvement rather than simple performance monitoring. To transform measurement into improvement initiatives:

Assess current organizational performance category across all four metrics:
1. Determine whether your organization performs at Low, Medium, High, or Elite levels
2. Identify metrics where performance lags behind others for balanced improvement
3. Understand which metrics most impact your organizational goals and priorities
Prioritize improvement initiatives based on:
1. Business impact of performance gains (faster time to market vs. improved reliability)
2. Technical feasibility of specific improvements (automation investments vs. process changes)
3. Organizational readiness for change (team skills, management support, cultural factors)
Set realistic improvement targets using DORA benchmark progression:
1. Aim for incremental movement between performance categories rather than dramatic leaps
2. Focus on sustaining current high performance while improving lagging metrics
3. Account for organizational context and constraints in target setting
Plan measurement cadence and tracking:
1. Establish regular DORA metrics review cycles (weekly, monthly, quarterly)
2. Define success criteria and intermediate milestones for improvement initiatives
3. Create feedback loops between measurement data and improvement experiment results
Document baseline performance for future comparison:
1. Record current metric values and performance category assessments
2. Note organizational context affecting current performance (team size, technology constraints, process maturity)
3. Track improvement initiative impacts on metric trends over time

Regular DORA analysis enables data-driven development capability improvement that connects technical practices to business outcomes.

Troubleshoot DORA metrics issues

Address common issues when DORA metrics data is not appearing or appears incomplete in the dashboard.

Missing deployment data

DORA metrics require workflow annotations and environment configuration to track deployments.

Problem: DORA metrics show no deployment data

Solution: Verify workflow steps include kind: deploy annotations and specify target environments. Check that environments are configured and associated with components.

To resolve missing deployment data:

Open your workflow YAML files for review.

Confirm workflow steps include kind: deploy annotations:

jobs:
  deploy:
    steps:
    - name: Deploy to production
      uses: ...
      kind: deploy
      env: production▼

Verify deployment steps specify target environments using the env parameter.
Navigate to Configurations Environments to check environment configuration:
1. Verify environments exist for all deployment targets.
2. Confirm environments are associated with components.
3. Check that environment names in workflow annotations match configured environment names.

DORA calculations begin with workflow runs that include proper annotations and environment associations.

Incomplete metric calculations

DORA metrics depend on sufficient workflow execution data for accurate calculations.

To address incomplete metrics:

Ensure workflows with kind: deploy annotations have executed recently.
Check workflow run details for annotation recognition and successful completion.
Verify sufficient deployment history exists for meaningful metric calculations (minimum 5-10 deployments recommended).
Allow 10-15 minutes for data processing after workflow completion.

Regular workflow execution with proper annotations ensures consistent DORA metric population.